code: def cov(
      x1: paddle.Tensor,
      x2: paddle.Tensor = None,
      /,
      *,
      rowVar: bool = True,
      bias: bool = False,
      ddof: Optional[int] = None,
      fweights: Optional[paddle.Tensor] = None,
      aweights: Optional[paddle.Tensor] = None,
      dtype: Optional[paddle.dtype] = None,
  ) -> paddle.Tensor:
      if fweights is not None:
          fweights = fweights.astype("float64")
  
      if aweights is not None:
          aweights = aweights.astype("float64")
  
      if ddof is not None and ddof != int(ddof):
          raise ValueError("ddof must be an integer")
  
      if len(x1.shape) > 2:
          raise ValueError("x1 has more than 2 dimensions")
  
      if x2 is not None:
          if len(x2.shape) > 2:
              raise ValueError("x2 has more than 2 dimensions")
  
      if ddof is None:
          if bias == 0:
              ddof = 1
          else:
              ddof = 0
  
      if dtype is None:
          x1 = x1.astype("float64")
          if x2 is not None:
              x2 = x2.astype("float64")
      else:
          x1 = x1.astype(dtype)
          if x2 is not None:
              x2 = x2.astype(dtype)
  
      X = x1
      if not rowVar and X.shape[0] != 1:
          X = paddle.transpose(
              X, perm=tuple(range(len(X.shape) - 1, -1, -1))
          )
  
      if x2 is not None:
          if not rowVar and x2.shape[0] != 1:
              x2 = paddle.transpose(
                  x2, perm=tuple(range(len(x2.shape) - 1, -1, -1))
              )
          if len(x2.shape) > 1:
              X = paddle.concat([X, x2], axis=0)
          else:
              X = paddle.stack([X, x2], axis=0)
  
      if not rowVar:
          X = paddle.transpose(
              X, perm=tuple(range(len(X.shape) - 1, -1, -1))
          )
  
      return paddle.linalg.cov(
          X,
          rowvar=rowVar,
          ddof=ddof,
          fweights=fweights,
          aweights=aweights,
      )
  
imports: from typing import Optional, Union, Tuple, Sequence, Any
  import paddle
  import ivy.functional.backends.paddle
  import ivy
  from copy import deepcopy
  from ivy.func_wrapper import with_unsupported_device_and_dtypes, with_supported_dtypes
  from .None import backend_version
label_desc: Use image node_id 18 for calling a global function with example usage: cov() and returns: paddle